Spring Term Schedule
Spring 2026
| Number | Title | Instructor | Time |
|---|
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CSC 402-01
Gonzalo Mateos Buckstein
MW 3:25PM - 4:40PM
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The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks.
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CSC 409-01
Christopher Kanan
M 2:00PM - 3:15PM
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Many companies are working towards creating artificial general intelligence (AGI), i.e., systems capable of accomplishing any human intellectual task, and multiple companies aspire to create such systems within the next decade. WHile large language models (LLMs) do not yet have these capabilities, they exhibit "sparks" of AGI. In this course we review the concept of AGI and how we might assess a system to understand if it is an AGI. We will study the gaps in state-of-the-art systems and what would be needed for a system to be considered an AGI. We will also study AI ethics and safety, the socioeconomic implications of AGI, the likelihood of an intelligence explosion (i.e., the singularity), and existential risk due to AGI. Coursework includes readings, student presentations, and a project. Prerequisites: At least one course that covers neural networks, e.g., CSC 242, CSC 298/578, CSC 266/466, CSC 249/449, CSC 277/3477, or instructor permision. Students are expected to be familiar with Python, one or more deep learning toolboxes, deep learning, and machine learning more broadly. Students should have a solid understanding of large language models, e.g., GPT-based causal transformers and similar approaches, backpropagation, multi-layer perceptrons, transformers, convolutional neural networks, and neural network fine-tuning.
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CSC 416-01
Zhen Bai
TR 3:25PM - 4:40PM
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Human activity is embodied through all our sensorimotor capacities, immersed in our immediate physical and social surroundings. Augmented Reality (AR) and Virtual Reality (VR) technologies have shown vast potential to extend our sensorimotor and social experiences in 3D space. This undergraduate/graduate course aims to help students to develop understanding of key AR/VR concepts, principles, technologies, design practices and development tools. The students will obtain hands-on experiences of creating AR/VR applications that address real-world problems in domains such as education, collaboration, and quality of life, and understand technological and ethical implications of AR/VR such as human behavior sensing and ethical challenges.
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CSC 440-01
Monika Polak
TR 9:40AM - 10:55AM
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This course is designed to develop your understanding of the fundamental concepts in data mining and how they can be used to answer questions about real-world problems. These methods, and the underlying models, can be used to learn from existing data to make predictions about new data. The course will help you understand the mindset of creating a data project pipeline by covering topics on model development, causal inference, and traditional data mining. The class will both emphasize individual work and also team work by helping students solidify their knowledge through a final project.
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CSC 443-01
Ralf Haefner
TR 9:40AM - 10:55AM
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This is a seminar-style course for advanced undergraduate and graduate students covering multiple areas of computational neuroscience by weekly readings and student presentations. Many of the topics are deeper explorations of topics covered in BCSC 247 Introduction to Computational Neuroscience, focusing on the sensory system, decision-making, action selection and active inference, especially from a probabilistic and normative perspective. The reading list is somewhat flexible and adaptable to student interest. There is an opportunity for a final project but this is not required.
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CSC 443-02
Ralf Haefner
F 2:00PM - 3:15PM
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This is a rotating topics course that includes the study of both the computations performed by the brain and of computational models of neuronal responses. Primary focus will be on the visual system. Programming experience is required. This course is taught every other year, alternating with Intro to Computational Neuroscience. Prerequisite: CSC 441 or permission of instructor
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CSC 446-01
Daniel Gildea
TR 11:05AM - 12:20PM
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Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered include perceptrons, logistic regression, support vector machines, and neural networks. Directed and undirected graphical models. Numerical parameter optimization, including gradient descent, expectation maximization, and other methods. Introduction to reinforcement learning. Proofs covered as appropriate. Significant programming projects will be assigned. This course involves a lot of math and algorithms. You should know multivariable calculus, linear algebra, and some algorithms. No formal prerequisites but MATH 165, MATH 164, and CSC 242 strongly recommended.
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CSC 447-01
Hangfeng He
MW 10:25AM - 11:40AM
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This introductory course in Natural Language Processing (NLP) explores the AI techniques enabling computers to understand and communicate with humans. Divided into four main sections—Statistical NLP, Neural NLP, Foundation Models, and Advanced Topics—the curriculum covers essential language tasks, from syntax and semantics to complex applications like question answering. We'll also touch on current trends in Large Language Models, such as emergent abilities and human-alignment techniques. Ideal for those interested in the intersection of AI and language, the course tackles challenges at the word, sentence, and document levels.
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CSC 449-01
Jiebo Luo
TR 9:40AM - 10:55AM
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Introduction to computer vision, including camera models, basic image processing, pattern and object recognition, and elements of human vision. Specific topics include geometric issues, statistical models, Hough transforms, color theory, texture, and optic flow. CSC 449, a graduate-level course, requires additional readings and assignments.
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CSC 452-01
Yuhao Zhu
WF 3:25PM - 4:40PM
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Introduction to computer architecture and the layering of hardware/software systems. Topics include instruction set design; logical building blocks; computer arithmetic; processor organization; the memory hierarchy (registers, caches, main memory, and secondary storage); I/Obuses, devices, and interrupts; microcode and assembly language; virtual machines; the roles of the assembler, linker, compiler, and operating system; technological trends and the future of computing hardware. Several programming assignments required.
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CSC 454-01
George Ferguson
TR 3:25PM - 4:40PM
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Design and implementation of programming languages, with an emphasis on imperative languages and on implementation tradeoffs. In-depth examination of 'how programming languages work.' Topics include fundamental language concepts (names, values, types, abstraction, control flow); compilation and interpretation (syntactic and semantic analysis, code generation and optimization); major language paradigms (imperative, object-oriented, functional, logic-based, concurrent). Course projects include assignments in several different languages, with an emphasis on compilation issues.
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CSC 458-01
Michael Scott
MW 3:25PM - 4:40PM
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Principles of parallel and distributed systems, and the associated implementation and performance issues. Topics covered will include programming interfaces to parallel and distributed computing, interprocess communication, synchronization, and consistency models, fault tolerance and reliability, distributed process management, distributed file systems, multiprocessor architectures, parallel program optimization, and parallelizing compilers. Students taking this course at the 400 level will be required to complete additional readings and/or assignments.
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CSC 461-01
Eustrat Zhupa
MW 2:00PM - 3:15PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 463-01
Fatemeh Nargesian
MW 9:00AM - 10:15AM
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This course explores the internals of data engines. Topics covered will include the relational model; relational database design principles based on dependencies and normal forms; query execution; transactions; recovery; query optimization; parallel query processing; NoSQL.
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CSC 476-01
Yanan Guo
MW 10:25AM - 11:40AM
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This course delves into advanced topics in computer architecture such as out-of-order execution, speculative execution, cache protocols, and advanced memory techniques. It will also introduce the security problems caused by these designs, such as side-channel attacks, Rowhammer attacks, and Spectre/Meltdown vulnerabilities. Students will learn about the design of modern processors and their impact on performance and security.
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CSC 480-01
Kaave Hosseini
MW 2:00PM - 3:15PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-02
Kaave Hosseini
W 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-03
Kaave Hosseini
W 6:15PM - 7:30PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-04
Kaave Hosseini
R 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-09
Kaave Hosseini
R 3:25PM - 4:40PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 482-01
Eustrat Zhupa
TR 9:40AM - 10:55AM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-02
Eustrat Zhupa
T 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-03
Eustrat Zhupa
W 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-04
Eustrat Zhupa
M 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-05
Eustrat Zhupa
W 7:40PM - 8:55PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 483-01
Monika Polak
MW 11:50AM - 1:05PM
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In this course students will learn about the current state of Public Key Cryptography. In particular the NIST (National Institute of Standards and Technology) process to solicit, evaluate, and standardize one or more quantum-resistant public-key cryptographic algorithms will be discussed. Quantum-Resistant Cryptography (QRC) refers to cryptographic systems that are secure against both quantum and classical computers. Such systems may be achieved through classical (i.e. non-quantum) means. The security of many commonly used cryptographic protocols (especially public-key cryptosystems) would be compromised if general-purpose large-scale, fault-tolerant quantum computers became a reality. This course covers the consequences of Quantum Computing and why it poses a threat to currently used cryptographic systems, and then discusses potential Post-Quantum cryptosystems designed to be resistant to such attacks..
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CSC 485-01
Lane Hemaspaandra
MW 4:50PM - 6:05PM
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This course covers gems from the study of elections and the study of algorithms. Students do not need to have previous exposure to either of those topics. Most of the class's work is done via in-class, hands-on, team-based problem-solving, and discussion of the solutions. Topics vary between years, but typical topics include how to fairly apportion (e.g., assign to states their number of seats in the US House of Representatives); using algorithms to manipulate elections and using complexity to protect elections from manipulative attacks; and the interaction between algorithms and information content. The course is designed to be accessible to any student from CSC, ECON, MATH, or PSCI who likes tackling problems as part of a team and discussing the solutions
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CSC 488-01
Kaave Hosseini
MW 4:50PM - 6:05PM
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In this course we introduce a number of tools with analytic flavor that have an impressive range of applications in computer science and discrete mathematics. Although the objects we are dealing with (such as graphs, sets, boolean functions, and various models of computation) are discrete in nature, it turns out that an analytic point of view—as opposed to a purely combinatorial one—provides a powerful lens to study these objects.We start with elementary tools such as probabilistic concentration bounds and then move on to more advanced material such as spectral graph theory, discrete Fourier analysis, and pseudorandomness. Applications of these tools in areas such as algorithms, coding theory, complexity theory, discrete mathematics, and machine learning will be discussed.
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CSC 491-01
Michael Scott
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This course is for master's students that have made arrangements with a faculty member to complete readings and discussion in a particular subject in their field of study.
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CSC 495-01
James Allen
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-02
John Criswell
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-03
Zhen Bai
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-04
Chen Ding
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-05
Sandhya Dwarkadas
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-06
Daniel Gildea
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-07
Lane Hemaspaandra
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-08
Ehsan Hoque
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-09
Henry Kautz
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-10
Jiebo Luo
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-11
Fatemeh Nargesian
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-12
Christopher Kanan
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-13
Sreepathi Pai
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-14
Lenhart Schubert
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-15
Michael Scott
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-16
Daniel Stefankovic
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-17
Muthu Venkitasubramaniam
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-18
Chenliang Xu
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-19
Yuhao Zhu
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-20
Aaron White
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-21
Thomas Howard
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-22
Zhiyao Duan
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-23
Yanan Guo
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 495-24
Yukang Yan
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This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.
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CSC 579-02
Chen Ding
R 9:40AM - 10:55AM
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This seminar teaches the theoretical equivalence between programming languages and mathematical logic. Topics include functional programming, proof by induction, proof tactics, logic, inductively defined propositions, and the Curry-Howard Correspondence. The interactive theorem prover called Coq is used to write machine-checked proofs. Students enrolling in the course are expected to have advanced knowledge in either programming languages (CSC 253, 254 or 255) or mathematical logic.
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CSC 595-01
John Criswell
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-02
Jiaming Liang
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-03
Zhen Bai
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-04
Chen Ding
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-05
Sandhya Dwarkadas
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-06
Daniel Gildea
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-07
Lane Hemaspaandra
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-08
Ehsan Hoque
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-09
Yukang Yan
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-10
Jiebo Luo
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-11
Fatemeh Nargesian
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-12
Ji Liu
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-13
Sreepathi Pai
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-14
Zhiyao Duan
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-15
Michael Scott
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-16
Daniel Stefankovic
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-17
Hangfeng He
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-18
Chenliang Xu
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-19
Yuhao Zhu
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-20
Aaron White
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-21
Xiangxiang Xu
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-22
Thomas Howard
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-23
Gregory Heyworth
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-24
Christopher Kanan
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-25
Mujdat Cetin
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-26
Anson Kahng
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-28
Kaave Hosseini
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 595-29
Adam Sefkow
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This course provides PhD students with fewer than 90 credits the opportunity to conduct, develop, and refine their doctoral research projects. Students will engage in research relevant to their field of study and make progress toward completing their dissertations.
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CSC 597-01
Anson Kahng
M 11:50AM - 1:05PM
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No description
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CSC 895-01
Daniel Gildea
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This course is designed for master's degree students who have completed all required coursework but still need to finalize specific degree requirements under less than half-time enrollment.
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CSC 897-01
Daniel Gildea
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This course provides master's students who are currently completing their final required coursework, or with special circumstances like an approved reduced courseload, with the opportunity to work full-time on their degrees. Students will make significant progress toward completing their degrees.
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CSC 899-01
Daniel Gildea
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This course provides master’s students who have completed or are currently completing all course requirements with the opportunity to work full-time on their thesis. Students will make significant progress toward completing their degrees.
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CSC 986V-01
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This course affords graduate students visiting the University of Rochester full-time student status. Visiting students will engage in research, discussion, and/or professional training in partnership with an academic department or faculty member.
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CSC 999-01
Daniel Gildea
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This course provides PhD students who have completed or are currently completing 90 credits of coursework and have fulfilled all degree requirements (except for the dissertation) with the opportunity to work full-time on their dissertation. Students will make significant progress toward completing their degrees.
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Spring 2026
| Number | Title | Instructor | Time |
|---|---|
| Monday | |
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CSC 597-01
Anson Kahng
M 11:50AM - 1:05PM
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No description
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CSC 409-01
Christopher Kanan
M 2:00PM - 3:15PM
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Many companies are working towards creating artificial general intelligence (AGI), i.e., systems capable of accomplishing any human intellectual task, and multiple companies aspire to create such systems within the next decade. WHile large language models (LLMs) do not yet have these capabilities, they exhibit "sparks" of AGI. In this course we review the concept of AGI and how we might assess a system to understand if it is an AGI. We will study the gaps in state-of-the-art systems and what would be needed for a system to be considered an AGI. We will also study AI ethics and safety, the socioeconomic implications of AGI, the likelihood of an intelligence explosion (i.e., the singularity), and existential risk due to AGI. Coursework includes readings, student presentations, and a project. Prerequisites: At least one course that covers neural networks, e.g., CSC 242, CSC 298/578, CSC 266/466, CSC 249/449, CSC 277/3477, or instructor permision. Students are expected to be familiar with Python, one or more deep learning toolboxes, deep learning, and machine learning more broadly. Students should have a solid understanding of large language models, e.g., GPT-based causal transformers and similar approaches, backpropagation, multi-layer perceptrons, transformers, convolutional neural networks, and neural network fine-tuning.
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CSC 482-04
Eustrat Zhupa
M 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 463-01
Fatemeh Nargesian
MW 9:00AM - 10:15AM
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This course explores the internals of data engines. Topics covered will include the relational model; relational database design principles based on dependencies and normal forms; query execution; transactions; recovery; query optimization; parallel query processing; NoSQL.
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CSC 447-01
Hangfeng He
MW 10:25AM - 11:40AM
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This introductory course in Natural Language Processing (NLP) explores the AI techniques enabling computers to understand and communicate with humans. Divided into four main sections—Statistical NLP, Neural NLP, Foundation Models, and Advanced Topics—the curriculum covers essential language tasks, from syntax and semantics to complex applications like question answering. We'll also touch on current trends in Large Language Models, such as emergent abilities and human-alignment techniques. Ideal for those interested in the intersection of AI and language, the course tackles challenges at the word, sentence, and document levels.
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CSC 476-01
Yanan Guo
MW 10:25AM - 11:40AM
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This course delves into advanced topics in computer architecture such as out-of-order execution, speculative execution, cache protocols, and advanced memory techniques. It will also introduce the security problems caused by these designs, such as side-channel attacks, Rowhammer attacks, and Spectre/Meltdown vulnerabilities. Students will learn about the design of modern processors and their impact on performance and security.
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CSC 483-01
Monika Polak
MW 11:50AM - 1:05PM
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In this course students will learn about the current state of Public Key Cryptography. In particular the NIST (National Institute of Standards and Technology) process to solicit, evaluate, and standardize one or more quantum-resistant public-key cryptographic algorithms will be discussed. Quantum-Resistant Cryptography (QRC) refers to cryptographic systems that are secure against both quantum and classical computers. Such systems may be achieved through classical (i.e. non-quantum) means. The security of many commonly used cryptographic protocols (especially public-key cryptosystems) would be compromised if general-purpose large-scale, fault-tolerant quantum computers became a reality. This course covers the consequences of Quantum Computing and why it poses a threat to currently used cryptographic systems, and then discusses potential Post-Quantum cryptosystems designed to be resistant to such attacks..
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CSC 461-01
Eustrat Zhupa
MW 2:00PM - 3:15PM
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This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.
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CSC 480-01
Kaave Hosseini
MW 2:00PM - 3:15PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 402-01
Gonzalo Mateos Buckstein
MW 3:25PM - 4:40PM
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The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks.
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CSC 458-01
Michael Scott
MW 3:25PM - 4:40PM
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Principles of parallel and distributed systems, and the associated implementation and performance issues. Topics covered will include programming interfaces to parallel and distributed computing, interprocess communication, synchronization, and consistency models, fault tolerance and reliability, distributed process management, distributed file systems, multiprocessor architectures, parallel program optimization, and parallelizing compilers. Students taking this course at the 400 level will be required to complete additional readings and/or assignments.
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CSC 485-01
Lane Hemaspaandra
MW 4:50PM - 6:05PM
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This course covers gems from the study of elections and the study of algorithms. Students do not need to have previous exposure to either of those topics. Most of the class's work is done via in-class, hands-on, team-based problem-solving, and discussion of the solutions. Topics vary between years, but typical topics include how to fairly apportion (e.g., assign to states their number of seats in the US House of Representatives); using algorithms to manipulate elections and using complexity to protect elections from manipulative attacks; and the interaction between algorithms and information content. The course is designed to be accessible to any student from CSC, ECON, MATH, or PSCI who likes tackling problems as part of a team and discussing the solutions
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CSC 488-01
Kaave Hosseini
MW 4:50PM - 6:05PM
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In this course we introduce a number of tools with analytic flavor that have an impressive range of applications in computer science and discrete mathematics. Although the objects we are dealing with (such as graphs, sets, boolean functions, and various models of computation) are discrete in nature, it turns out that an analytic point of view—as opposed to a purely combinatorial one—provides a powerful lens to study these objects.We start with elementary tools such as probabilistic concentration bounds and then move on to more advanced material such as spectral graph theory, discrete Fourier analysis, and pseudorandomness. Applications of these tools in areas such as algorithms, coding theory, complexity theory, discrete mathematics, and machine learning will be discussed.
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| Tuesday | |
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CSC 482-02
Eustrat Zhupa
T 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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| Tuesday and Thursday | |
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CSC 440-01
Monika Polak
TR 9:40AM - 10:55AM
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This course is designed to develop your understanding of the fundamental concepts in data mining and how they can be used to answer questions about real-world problems. These methods, and the underlying models, can be used to learn from existing data to make predictions about new data. The course will help you understand the mindset of creating a data project pipeline by covering topics on model development, causal inference, and traditional data mining. The class will both emphasize individual work and also team work by helping students solidify their knowledge through a final project.
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CSC 443-01
Ralf Haefner
TR 9:40AM - 10:55AM
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This is a seminar-style course for advanced undergraduate and graduate students covering multiple areas of computational neuroscience by weekly readings and student presentations. Many of the topics are deeper explorations of topics covered in BCSC 247 Introduction to Computational Neuroscience, focusing on the sensory system, decision-making, action selection and active inference, especially from a probabilistic and normative perspective. The reading list is somewhat flexible and adaptable to student interest. There is an opportunity for a final project but this is not required.
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CSC 449-01
Jiebo Luo
TR 9:40AM - 10:55AM
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Introduction to computer vision, including camera models, basic image processing, pattern and object recognition, and elements of human vision. Specific topics include geometric issues, statistical models, Hough transforms, color theory, texture, and optic flow. CSC 449, a graduate-level course, requires additional readings and assignments.
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CSC 482-01
Eustrat Zhupa
TR 9:40AM - 10:55AM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 446-01
Daniel Gildea
TR 11:05AM - 12:20PM
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Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered include perceptrons, logistic regression, support vector machines, and neural networks. Directed and undirected graphical models. Numerical parameter optimization, including gradient descent, expectation maximization, and other methods. Introduction to reinforcement learning. Proofs covered as appropriate. Significant programming projects will be assigned. This course involves a lot of math and algorithms. You should know multivariable calculus, linear algebra, and some algorithms. No formal prerequisites but MATH 165, MATH 164, and CSC 242 strongly recommended.
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CSC 416-01
Zhen Bai
TR 3:25PM - 4:40PM
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Human activity is embodied through all our sensorimotor capacities, immersed in our immediate physical and social surroundings. Augmented Reality (AR) and Virtual Reality (VR) technologies have shown vast potential to extend our sensorimotor and social experiences in 3D space. This undergraduate/graduate course aims to help students to develop understanding of key AR/VR concepts, principles, technologies, design practices and development tools. The students will obtain hands-on experiences of creating AR/VR applications that address real-world problems in domains such as education, collaboration, and quality of life, and understand technological and ethical implications of AR/VR such as human behavior sensing and ethical challenges.
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CSC 454-01
George Ferguson
TR 3:25PM - 4:40PM
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Design and implementation of programming languages, with an emphasis on imperative languages and on implementation tradeoffs. In-depth examination of 'how programming languages work.' Topics include fundamental language concepts (names, values, types, abstraction, control flow); compilation and interpretation (syntactic and semantic analysis, code generation and optimization); major language paradigms (imperative, object-oriented, functional, logic-based, concurrent). Course projects include assignments in several different languages, with an emphasis on compilation issues.
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| Wednesday | |
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CSC 480-02
Kaave Hosseini
W 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-03
Kaave Hosseini
W 6:15PM - 7:30PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 482-03
Eustrat Zhupa
W 6:15PM - 7:30PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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CSC 482-05
Eustrat Zhupa
W 7:40PM - 8:55PM
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How does one design programs and ascertain their efficiency? Divide-and-conquer techniques, string processing, graph algorithms, mathematical algorithms. Advanced data structures such as balanced tree schemes. Introduction to NP-completeness and intractable combinatorial search, optimization, and decision problems.
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| Wednesday and Friday | |
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CSC 452-01
Yuhao Zhu
WF 3:25PM - 4:40PM
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Introduction to computer architecture and the layering of hardware/software systems. Topics include instruction set design; logical building blocks; computer arithmetic; processor organization; the memory hierarchy (registers, caches, main memory, and secondary storage); I/Obuses, devices, and interrupts; microcode and assembly language; virtual machines; the roles of the assembler, linker, compiler, and operating system; technological trends and the future of computing hardware. Several programming assignments required.
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| Thursday | |
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CSC 579-02
Chen Ding
R 9:40AM - 10:55AM
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This seminar teaches the theoretical equivalence between programming languages and mathematical logic. Topics include functional programming, proof by induction, proof tactics, logic, inductively defined propositions, and the Curry-Howard Correspondence. The interactive theorem prover called Coq is used to write machine-checked proofs. Students enrolling in the course are expected to have advanced knowledge in either programming languages (CSC 253, 254 or 255) or mathematical logic.
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CSC 480-09
Kaave Hosseini
R 3:25PM - 4:40PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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CSC 480-04
Kaave Hosseini
R 4:50PM - 6:05PM
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This course studies fundamental computer models and their computational limitations. Finite-state machines and pumping lemmas, the context-free languages, Turing machines, decidable and Turing-recognizable languages, undecidability, NP-completeness.
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| Friday | |
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CSC 443-02
Ralf Haefner
F 2:00PM - 3:15PM
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This is a rotating topics course that includes the study of both the computations performed by the brain and of computational models of neuronal responses. Primary focus will be on the visual system. Programming experience is required. This course is taught every other year, alternating with Intro to Computational Neuroscience. Prerequisite: CSC 441 or permission of instructor
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